وظائف أخصائى مكتبات - الدار البيضاء المغرب

<p><font dir="auto"><font dir="auto">As a </font><em><strong><font dir="auto">Senior Data Scientist</font></strong></em><font dir="auto"> , you will be responsible for the following tasks:</font></font><em> <strong><font dir="auto"></font></strong> </em><font dir="auto"></font></p> <p> <strong><font dir="auto"><font dir="auto">- Development of predictive and prescriptive models:</font></font></strong> </p> <ul> <li><font dir="auto"></font><strong><font dir="auto"><font dir="auto">Design and train Customer Segmentation</font></font></strong><font dir="auto"><font dir="auto"> models </font><font dir="auto">(clustering, RFM, behavioral profiling).</font></font></li> <li><font dir="auto"></font><strong><font dir="auto"><font dir="auto">Develop personalized recommendation</font></font></strong><font dir="auto"><font dir="auto"> engines </font><font dir="auto">(collaborative filters, content-based, hybrid systems) to improve cross-selling and upselling.</font></font></li> <li> <strong><font dir="auto"><font dir="auto">Developing Prediction and Detection Models:</font></font></strong><font dir="auto"><font dir="auto"> Churn, propensity, risk scoring, transaction volume forecasting </font></font><strong><font dir="auto"><font dir="auto">and detection of anomalies / behavioral rules for the fight against money laundering (AML / Anti Money Laundering)</font></font></strong> </li> </ul> <p> <strong><font dir="auto"><font dir="auto">- </font></font></strong> <strong><font dir="auto"><font dir="auto">Data and model engineering:</font></font></strong> </p> <ul> <li><font dir="auto"><font dir="auto">Collaborating with Data Engineers to define features and prepare the necessary training pipelines from the Datalakehouse (Cassandra, Hive, etc.).</font></font></li> <li><font dir="auto"><font dir="auto">Implement </font></font><strong><font dir="auto"><font dir="auto">MLOps</font></font></strong><font dir="auto"><font dir="auto"> best practices : model versioning, deployment automation, production performance monitoring (drift, bias).</font></font></li> </ul> <p> <strong><font dir="auto"><font dir="auto">- Added value to the business:</font></font></strong> </p> <ul> <li><font dir="auto"><font dir="auto">Translate business needs (e.g., reduce churn, increase conversion rate) into Data Science problems.</font></font></li> <li><font dir="auto"><font dir="auto">Interpret the model results and communicate the insights to Product Owners and business stakeholders.</font></font></li> <li><font dir="auto"><font dir="auto">Contribute to the enrichment of source systems and the Datalakehouse with predictive data.</font></font></li></ul><p><strong>Desired Candidate Profile</strong></p><strong>Qualifications </strong><p></p> <ul> <li> <strong><font dir="auto"><font dir="auto">Education:</font></font></strong><font dir="auto"><font dir="auto"> Engineering degree (Bac+5) with a specialization in Data Science, Artificial Intelligence or Statistics/Applied Mathematics.</font></font></li> <li> <strong><font dir="auto"><font dir="auto">Experience:</font></font></strong><font dir="auto"><font dir="auto"> 35 years of confirmed experience on similar predictive modeling and recommendation systems projects.</font></font></li> <li> <strong>Comp tences Techniques :</strong> </li> <li> <strong><font dir="auto"><font dir="auto">Mastery of ML:</font></font></strong><font dir="auto"><font dir="auto"> Classical algorithms (regression, classification, clustering, time series) and Deep Learning (if applicable).</font></font></li> <li> <strong>Frameworks IA :</strong> Ma trise des biblioth ques Python (Scikit-learn, Pandas, XGBoost, TensorFlow ou PyTorch).</li> <li> <strong><font dir="auto"><font dir="auto">Big Data &amp; Tools:</font></font></strong><font dir="auto"><font dir="auto"> Knowledge of distributed environments (Spark/PySpark) and NoSQL databases (Cassandra, MongoDB).</font></font></li> <li> <strong><font dir="auto"><font dir="auto">Ops &amp; CI/CD:</font></font></strong><font dir="auto"><font dir="auto"> Familiarity with containerization environments (Docker) and model lifecycle management (MLOps).</font></font></li> <li> <strong><font dir="auto"><font dir="auto">technological environment:</font></font></strong><font dir="auto"><font dir="auto"> Cloudera Data Platform, Microservices Architecture, Kafka, API Gateway (Axway), Keycloak.</font></font></li> <li> <strong><font dir="auto"><font dir="auto">Methodology:</font></font></strong><font dir="auto"><font dir="auto"> Working in Agile mode (Scrum of Scrums, weekly follow-up committees).</font></font></li> <li> <strong>Collaboration :</strong> quipe pluridisciplinaire (Data Engineers, Lead Tech, Product Owners, Experts m tier).</li></ul>